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Enhanced forecasting of friction and cohesion of augmented unsaturated soil with nanostructured quarry fines (NQF) addition
(2026-12-01)
Kamchoom, Viroon
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Van, Duc Bui
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Hosseini, Shahab
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Alimoradijazi, Mohammadreza
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Amini-Khoshalan, Hasel
Shear strength parameters such as friction angle and cohesion are fundamental to solving geotechnical engineering problems related to slope stability, foundation design, and earthwork construction. This study presents the prediction of friction angle (Fi) and cohesion (Nc) of an unsaturated lateritic soil using three intelligent learning techniques: Support Vector Machine (SVM), Radial Basis Function (RBF), and Multilayer Perceptron (MLP), with Linear Multivariate Regression (LMR) adopted as a baseline model to evaluate agreement between input and output variables. The motivation for employing machine learning approaches stems from the limitations of complex laboratory testing and the need for reliable predictive tools that can support design and field applications. The investigated soil, classified as A-7-6 and poorly graded, exhibited coefficients of uniformity and curvature of 2.05 and 0.84, respectively. It was characterized by high plasticity and significant clay content, with a clay fraction of 23.02%, clay activity of 2, friction angle of 15°, maximum dry density of 1.84 g/cm3 at an optimum moisture content of 16.2%, and was tested under cyclic direct shear conditions. Multiple datasets were generated from varying treatment conditions and soil descriptors, forming the basis for model development. Eleven input parameters were used to predict Fi and Nc, and model performance was evaluated using Variance Accounted For (VAF), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2). The results indicate that RBF and MLP outperformed SVM and LMR in both training and testing phases for predicting cohesion and friction angle, demonstrating superior generalization capability. Sensitivity analysis using the Cosine Domain Method revealed that unsaturated unit weight had the greatest influence on friction angle prediction, while clay content was the most influential parameter for cohesion. Among all models, MLP achieved the highest accuracy and overall predictive performance. Based on this optimal model, a Graphical User Interface was developed to enable users to input soil parameters and obtain rapid predictions, providing a practical tool for researchers and practitioners in geotechnical engineering.
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Photothermal solar assisted Madhuca diethyl ether fuel processing for LHR engines with AI-based performance and yield prediction
(2026-12-01)
Dubey, Rakesh
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Prajapati, Ajeet Kumar
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Bharadwaj, Shruti
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Kamchoom, Viroon
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Onyelowe, Kennedy C.
This study investigates the combustion, performance, and emission characteristics of biodiesel blends derived from Madhuca longifolia oil with diethyl ether (DEE) as an oxygenated additive in a diesel engine. Prior to fuel preparation, Fourier Transform Infrared (FTIR) analysis was conducted to verify the chemical composition of the extracted oil, confirming the presence of triglyceride structures and long-chain fatty acids characteristic of Madhuca longifolia oil. A solar-assisted preheating mechanism was incorporated during oil extraction to reduce energy consumption and improve yield consistency. The system was further integrated with a 250 Wp solar photovoltaic (PV) panel (efficiency ~ 17%, Voc = 37 V, Isc = 8.5 A, MPPT = 30 V/8 A) to power auxiliary loads such as the fuel metering unit, sensors, and control panel. This renewable integration enabled 100% solar contribution for auxiliary components, saving approximately 1.04 kWh/day of grid electricity and achieving an estimated reduction of about 151 kg of CO<inf>2</inf> emissions annually. Four fuel types were evaluated: Diesel, MB100 (pure biodiesel), MB20D80 (20% biodiesel, 80% diesel), and MB5DEE5D90 (5% biodiesel, 5% DEE, 90% diesel). Among these, MB5DEE5D90 demonstrated comparatively improved performance, showing an 8% increase in Brake Thermal Efficiency (BTE) and a 10% reduction in Brake-Specific Fuel Consumption (BSFC) compared with diesel. Emission analysis indicated reductions of approximately 20% in CO and 18% in HC emissions, while life-cycle assessment suggested around 40% lower combustion-phase CO<inf>2</inf> emissions. Heat release rate analysis indicated earlier and more efficient combustion behavior. Additionally, LSTM-based predictive modeling showed lower error margins compared with RNN, demonstrating improved prediction accuracy for engine performance parameters.
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Investigation of long-term performance monitoring of cementitious mixes modified with healing agents and polymeric additives of self-healing polymer modified mortar (SHPMM)
(2026-03-01)
Kanwal, Humaira
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Wang, Ziping
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Hao, Wenfeng
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Javed, Kamran
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Asim, Muhammad
Concrete and mortar exhibit durability limitations in aggressive environments due to cracking, high permeability, and construction defects. Polymer-modified and self-healing cementitious materials have emerged as sustainable solutions; however, the synergistic use of polymer modifiers with chemical–biological healing agents remains underexplored. This study investigates self-healing polymer-modified mortar (SHPMM) incorporating styrene butadiene rubber (SBR) and ethylene vinyl acetate (EVA) as partial cement replacements at 0%,4%,8%,12% & 16%. A healing system consisting of 5% calcium lactate, 5% sodium silicate, 1% sodium carbonate. Also 1% effective microorganisms was added to all mixes. Workability, mechanical performance, durability, and microstructural characteristics were evaluated through slump, ultrasonic pulse velocity, strength tests, rapid chloride permeability, SEM, and EDX analyses. The results indicate that polymer addition significantly improves workability, strength, and durability. Slump values increased steadily with increasing polymer content. Optimum performance was observed at 4% and 8% polymer replacement, where permeability was markedly reduced. Compared to the control mix, compressive strength increased by 7–11%, split tensile strength by 12–17%, and flexural strength by 31–33%. RCPT values decreased substantially, with reductions of 32% and 45% for 4% and 8% SBR, and 22% and 58% for 4% and 8% EVA, respectively. Microstructural analysis confirmed improved matrix densification and crack-healing efficiency. EVA demonstrated superior performance compared to SBR, attributed to its powdered form and enhanced bonding characteristics. Overall, the combined application of polymer modifiers and healing agents effectively improves the mechanical performance, durability, and self-healing efficiency of cementitious composites, offering a viable solution for sustainable infrastructure in aggressive environments.
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Development of data-driven framework for the geotechnical behavior of xanthan gum-treated clay reinforced with polypropylene fibers
(2026-01-01)
Onyelowe, Kennedy C.
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Kamchoom, Viroon
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Baldovino, Jair De Jesús Arrieta
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Kumar, S. Anandha
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Ebid, Ahmed M.
This study develops a robust data-driven modeling framework to predict the unconfined compressive strength (UCS) and stiffness (Go) of low-plasticity clay soils stabilized with Xanthan Gum (XG) and Polypropylene Fiber (PPF), aiming to advance sustainable geotechnical design. A total of 108 soil specimens were prepared with varying XG dosages and cured over different periods, and predictive models were constructed using a Decision Table algorithm optimized with six bio-inspired optimization techniques. Among these, the Firefly-optimized model consistently provided the highest accuracy, demonstrating reliable agreement between predicted and measured values. Sensitivity analysis identified XG dosage, curing time, and dry density as the most influential factors governing UCS and Go. These findings highlight the strong potential of the proposed machine learning framework to guide field engineers in optimizing mix design parameters for improved mechanical behavior of bio-treated soils, reducing reliance on time-consuming and costly laboratory tests while promoting environmentally sustainable foundation practices. The need for this study arises from the growing demand for green soil stabilization techniques that minimize the use of cement and lime while still ensuring reliable performance in construction. Its applicability extends to real-world geotechnical projects such as embankments, road subgrades, and shallow foundations, where predictive modeling can significantly streamline design decisions and improve long-term sustainability.
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Mechanical properties of self compacting concrete reinforced with hybrid fibers and industrial wastes under elevated heat treatment
(2025-12-01)
Onyelowe, Kennedy C.
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Hanandeh, Shadi
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Kamchoom, Viroon
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Ebid, Ahmed M.
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Zurita Polo, Susana Monserrat
Machine learning prediction of the mechanical properties of self-compacting concrete (SCC) reinforced with hybrid fibers, incorporating industrial wastes like fly ash and blast furnace slag, and cured under elevated heat provides a reliable and efficient alternative to traditional laboratory experiments. In this work, extensive literature review leading to the collection, sorting and curation of a global database representative of the mechanical properties of self-compacting concrete reinforced with hybrid fiber mixed with industrial wastes for sustainable construction was conducted. The collected database constituted traditional concrete components and admixtures such as Cement (C), Fly ash (FA), Slag (BFS), Fine Aggregate (FAg), Coarse Aggregate (CAg), Water (W), Superplasticizer (PL), Fiber (Fi), and Temperature (Temp.) studied under the mechanical properties such as the Compressive Strength (Fc), Tensile Strength (Fsp), and Flexural Strength (Ff). The collected 114 records were divided into training set (90 records = 80%) and validation set (24 records = 20%) following the guidelines for data partitioning for optimal performance in machine learning predictions. Different advanced machine learning methods created using “Weka Data Mining” software version 3.8.6 were applied such as “Semi-supervised classifier (Kstar)”, “M5 classifier (M5Rules), “Elastic net classifier (ElasticNet), “Correlated Nystrom Views (XNV)”, and “Decision Table (DT)” to predict the output. The Hoffman/Gardener and SHAP techniques are used to estimate the sensitivity of the input parameter on the output. Finally, various performance metrics are used to evaluate the reliability of the models. The results show that the machine learning models show varying degrees of predictive accuracy, with the Kstar and XNV models consistently outperforming others across all mechanical properties. However, Kstar with accuracies of 96.5%, 96.0%, and 97.0% for Fc, Fsp, and Ff predictions, respectively proposed the most decisive model. Also, the Hoffman and Gardener method highlights the role of the binders, chemical additives, and curing, whereas SHAP attributes greater importance to aggregates and binder interactions.
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Data-driven framework for prediction of mechanical properties of waste glass aggregates concrete
(2025-12-01)
Onyelowe, Kennedy C.
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Hanandeh, Shadi
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Kamchoom, Viroon
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Ebid, Ahmed M.
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Imran, Hamza
This research presents a novel data-driven framework for predicting the mechanical properties of waste glass aggregate concrete using six advanced metaheuristic optimization algorithms: Bat Algorithm (Bat), Cuckoo Search Algorithm (Cuckoo), Elephant Herding Optimization (Elephant), Firefly Algorithm (Firefly), Rhinoceros Optimization Algorithm (Rhino), and Gray Wolf Optimizer (Wolf). The study evaluates these models based on their ability to predict compressive strength (Fc), tensile strength (Ft), density, and slump using key statistical performance indicators such as SSE, MAE, MSE, RMSE, accuracy, R<sup>2</sup>, and KGE. Sensitivity analysis was conducted using Hoffman and Gardener’s method as well as the SHAP technique to determine the most influential parameter in the prediction process. Results indicate that the Firefly and Wolf algorithms exhibited the highest prediction accuracy across all four properties, with Wolf emerging as the overall best-performing model due to its superior generalization ability, lower error rates, and high correlation with experimental results. Among the input parameters, the water-to-binder ratio was identified as the most influential factor affecting the mechanical properties of waste glass aggregate concrete, as demonstrated by both sensitivity analysis methods. This highlights the critical role of optimal water content in achieving desirable strength and workability in sustainable concrete mixtures. The study’s novelty lies in the comparative assessment of multiple optimization algorithms applied to waste-based concrete, an approach that has not been extensively explored in previous research. Additionally, the integration of SHAP analysis for feature importance ranking provides an interpretable machine learning approach to concrete mix design, which enhances decision-making for engineers and researchers. The practical implications of this research extend to sustainable machine learning-based concrete design, where AI-driven optimization can help reduce the reliance on conventional trial-and-error methods. By utilizing waste glass aggregates, the study supports circular economy initiatives in construction, reducing environmental impact while maintaining structural performance. The proposed models can be implemented in real-world scenarios to optimize mix designs for large-scale applications, leading to cost-effective and eco-friendly construction materials. This research advances the field of smart construction by demonstrating the effectiveness of machine learning in sustainable material engineering, paving the way for future AI-assisted innovations in the industry.
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Self compacting concrete with recycled aggregate compressive strength prediction based on gradient boosting regression tree with Bayesian optimization hybrid model
(2025-12-01)
Abood, Emad A.
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Thoeny, Zainab Abdulrdha
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Azize, Noralhuda M.
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Imran, Hamza
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Kamchoom, Viroon
Self-compacting concrete (SCC) is a special type of concrete that is used in applications requiring high workability, such as in densely reinforced or complex formwork situations. The estimation of 28-day compressive strength for this type is usually made by costly and time-consuming laboratory tests. The problem becomes even more complex when recycled aggregates are added to the mixture to promote eco-friendly and sustainable construction practices. In our research we presented a new hybrid model, GBRT, that was integrated with Bayesian Optimization. This model is able to accurately and efficiently estimate the compressive strength of SCC containing recycled aggregates. We evaluated the model using well-known performance metrics such as RMSE, MAE, and. The performance of the model gave us, on average, an RMSE of 6.000, MAE of 3.968, and of 0.806 in five-fold cross-validation, which emphasized its strong predictive capability and potential as a cost-effective alternative to conventional laboratory testing. The model was also compared with single learner models such as SVR and KNN in order to demonstrate the superiority of the hybrid approach in terms of prediction accuracy and robustness. Our hybrid model surpassed the two previously mentioned models when testing their performance on the test data. Since our model works as a black-box model, a novel explaining machine learning technique named SHAP (Shapley Additive Explanations) was employed to determine which predictors have the most importance and how they trend. The developed model is an accurate, fast, and economical substitute for predicting 28-day compressive strength of self-compacting concrete with recycled aggregates. Finally, the model is converted into an easy-to-use graphical interface that provides civil engineers and practitioners with a useful decision-support tool for mix design optimization and quality control in real-life construction projects.
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Effects of milling followed by different gradation sizes of lawrencepur sand on the properties of cementitious mortar
(2025-12-01)
Aslam, Hafiz Muhammad Shahzad
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Aslam, Hafiz Muhammad Usman
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Onyelowe, Kennedy C.
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Noshin, Sadaf
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Yasin, Mazhar
The swift rise of urbanization and industrialization has severely depleted natural sand resources and escalated industrial waste accumulation. Environmental damage from over-dredging and limited disposal space has driven researchers to seek alternative solutions. In Lahore, Punjab, Pakistan, coarse pit sand is predominantly used, and overly fine sands from the Ravi and Chenab rivers, which do not meet ASTM grading standards. In this research, Lawrencepur Sand was milled, and its effects on the sand's physical properties and the mortar's mechanical performance were evaluated. For this, comprehensive experimental investigations were conducted, and the mechanical properties of the mortar were assessed at 3, 7, and 28 days. From the experiment, it is clear that milling refines the physical properties of sand by decreasing size, fineness modulus (FM), and absorption, while increasing density and specific gravity, which enhances mortar performance. Milled sand improved mortar density (3.1–11.5 %), compressive strength (10.6–71.4 %), and flexural strength (13.5–48 %), while reducing water absorption by 9–34 %. Excessive milling reduced strength and increased water absorption. SPSS analysis confirmed that milled sand significantly improved mortar performance, with strong statistical validation (p < 0.001). Scanning Electron Microscopy (SEM) and X-ray Diffraction (XRD) analysis also confirmed microstructural densification of mortar by milling, and over-milling led to a decline in performance due to poor packing.
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Design of an integrated model for pothole detection and repair optimization using multimodal transformers and hybrid deep learning
(2025-12-01)
Meshram, Kundan
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Saurabh, Aryan
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Kharole, Vinay
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Chatrabhuj
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Mishra, Umank
The detection and timely repair of potholes are crucial for maintaining road safety and minimizing vehicle damage. However, existing methods often suffer from limitations such as reliance on single-modal data, poor generalization across diverse environments, and suboptimal resource management. To address these challenges, we propose a comprehensive framework for enhanced pothole detection and repair optimization using advanced deep learning techniques. Our approach integrates four key methodologies: Multimodal Enhanced Pothole Detection with Person-Level Data (M-E-Pot holeNet), Hybrid Machine Learning-Deep Learning for Classification (Hybrid-Pot holeNet), Deep Reinforcement Learning for Pot hole Detection and Repair Optimization (DRL-Pot holeOpt), and Transfer Learning for Pothole Detection in Diverse Environments (TL-Pot holeAdaptNet). M-E-Pot holeNet employs a Self-Supervised Multimodal Transformer (SSMT) to fuse camera, accelerometer, and crowdsourced smartphone data, achieving robust detection with a 97 % accuracy and under 2 % false positive rate. Hybrid-Pot holeNet combines Graph Attention Networks (GAT) and XGBoost, modeling spatial road features to classify potholes with 95 % accuracy and an F1-Score of 0.92. DRL-Pot holeOpt uses Soft Actor-Critic (SAC) with Bayesian Optimization to efficiently schedule repair tasks, reducing repair costs by up to 20 % and crew travel time by 15–25 %. Finally, TL-Pot holeAdaptNet leverages Domain-Adversarial Neural Networks (DANN) to ensure cross-domain adaptability, with 90 % accuracy in new environments and a 40–50 % reduction in domain discrepancy. This multi-faceted approach addresses the limitations of previous work by providing scalable, real-time, and resource-optimized solutions for pothole detection and maintenance, offering significant improvements in accuracy, cost efficiency, and adaptability.
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Modeling suction of unsaturated granular soil treated with biochar in plant microbial fuel cell bioelectricity system
(2025-12-01)
Onyelowe, K. C.
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Ebid, Ahmed M.
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Ramos Jiménez, Rosa Belén
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Kamchoom, Viroon
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Vishnupriyan, M.
There is an initiative driven by the carbon-neutrality nature of biochar in recent times, where various countries across Europe and North America have introduced perks to encourage the production of biochar for construction purposes. This objective aligns with the zero greenhouse emission targets set by COP27 for 2050. This research work seeks to assess the effectiveness of biochar in soils with varying grain size distributions in enhancing the soil–water characteristic curve (SWCC). This work further explores the effect of different combinations of biochar content (0 to 15 mass %) on the bioelectricity generation from biochar-improved plant microbial fuel cells (BPMFC). Additionally, different machine learning models such as the “Gradient Boosting (GB)”, “CN2 Rule Induction (CN2)”, “Naive Bayes (NB)”, “Support vector machine (SVM), “Stochastic Gradient Descent (SGD)”, “K-Nearest Neighbors (KNN)”, “Tree Decision (Tree)”, “Random Forest (RF)”, and “Response Surface Methodology” (RSM), have been developed to predict SWCC based on soil suction, electric current, electrical potential, volumetric water content, temperature, and bulk density. The newly established model demonstrates a reasonable ability to predict SWCC and a cheaper technology in predicting the suction of unsaturated soils in relation to the studied bioelectric factors of the BPMFC. Overall, in this research paper, the GB, SVM and CN2 outclassed the other regression techniques in this order thereby proposing the cheapest technology with the highest performance index to predict the SWCC behavior of unsaturated soils in a BPMFC system.

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